The search engine technology carries out a revolution around the knowledge graph. The core of the revolution turns to a new generation knowledge-based search oriented to structuralized knowledge from the traditional search oriented to un-structuralized webpage text, thus providing more accurate search results for the user and improving the user's search experience. At present, the core of the knowledge graph technology (the knowledge graph in a narrow sense) include the entity knowledge base (hereinafter referred to as the entity library) containing the entity knowledge and the related application technologies.
In the entity library, one piece of entity knowledge may consist of its unique semantic serial number i.e. a semantic identifier (ID), attributes with the clear semantic meaning and property values. For example, for the entity knowledge “BAIDU Co.”, the entity library first gives a globally unique ID (assumed as company_XXX) as its unique semantic serial number, and adds semantic information based on categories which the entity belongs to, for example, “Founded: January 2000”, “Business: Network Information Service”, “Chairman: Li Yanhong”, “subsidiaries: 91 Wireless (company_YYY), IQI (company_ZZZ)”, etc. The above knowledge may solve the user's questions about factual knowledge, for example, “what are subsidiaries of BAIDU Co.?” or “who is the Chairman of BAIDU Co.?”. It just needs to resolve the above questions to “{category=company; entity=BAIDU Co. (company_XXX); attribute=subsidiaries; attribute value=?}” and “{category=company; entity=BAIDU Co. (company_XXX); attribute=Chairman; attribute value=?}”, such that the user's requirements may be satisfied by the existing knowledge graph.
However, since the entity knowledge is centered on an entity or a certain virtual entity, when describing relationships between entities, one of the most common methods is to abstract it into a specific attribute, for example, “Chairman”, “subsidiaries”, “Business”, etc. Although such abstraction may make the representation of knowledge more refined, details may be lost.
Taking the in-depth ask-answer application as example—if the user asks “which subsidiaries are acquired by BAIDU Co. in 2013” or “how much does BAIDU Co. acquire 91 Wireless and internet TV software (PPS) totally”, the above questions cannot be answered based on the knowledge of the traditional entity library.
Thus, when the in-depth knowledge search is performed based on the traditional entity library, no search results can be provided for the user, thereby reducing the user's search experience.